Through decades of support, Microsoft was an instrumental force helping China become the AI powerhouse it is today. Now, as the very thought of a U.S. company partnering in tech projects in China draws scrutiny from lawmakers, national security hawks, and human rights advocates, Microsoft could be forced to grapple with tough decisions surrounding the thriving AI ecosystem it fostered there. Microsoft established its research lab in Beijing in 1998, when it was a pioneer paving the way for AI research and business collaborations between the U.S. and China. It was three years before China joined the World Trade Organization, a time when President Bill Clinton actively pushed for closer trade ties with the country, and when AI was mostly the stuff of sci-fi pipe dreams. Since then, Microsoft Research Asia, or MSRA, has been known as one of the most influential hubs of AI research in the world, advancing speech recognition, natural language and image processing, and other deep-learning work, spreading its discoveries far and wide. Elements of research conducted at MSR China have been used to build Microsoft's advertising, chatbots, Bing search, Windows, Xbox, Azure Cloud, and other products used everywhere.

Q: How do you define AI in the spectrum of technology? John Edwards, vice president, Healthcare Solutions Consulting, SoftServe: We talk about artificial intelligence (AI). It's still a pipe dream that a robot would be thinking and feeling and being able to replace what a human brain does. But what AI was originally intended to do was to automate some set of activities that a person would typically do that you can create rules using computers to be able to replace that and do it more efficiently. And so, it is evolved to be a set of techniques that have their roots in statistics.

The face of a Connecticut farmer thought to be a vampire when he died of tuberculosis in the 19th century has been seen for the first time since his corpse was mutilated and tossed into a grave. The disease turns people's skin a pale yellow, their eyes become red and swollen and they sometimes have bloodstains around their mouth from coughing, which was believed to be signs of the undead about 200 years ago. The man's skeleton, buried in a casket with'JB55' engraved on it, was used to performed a DNA analysis that was fed to a machine learning system to predict what he may have looked like before being riddled with the disease. The results showed he had fair skin, brown or hazel eyes, brown or black hair and some freckles. The man, a farmer who lived in Connecticut, died of tuberculosis in the 19th century, which led people to believe he was a vampire.

Ukraine has developed drones that are capable of finding targets autonomously, a Ukrainian military leader has claimed, raising the prospect that the ongoing Russia-Ukraine war could see the first confirmed use of'killer robots' in armed conflict. Ukrainian Lieutenant Colonel Yaroslav Honchar gave details in an interview with Ukrainian news agency UNIAN on 13th October. Honchar is co-founder of Aerorozvidka ("Aerial Intelligence"), a team of around a thousand volunteer drone enthusiasts and technologists set up in 2014 to develop and use drones and other technology. Honchar says their drones already fly scout missions autonomously and mentions the possibility of automated strikes, but did not say such strikes had been carried out. Aerorozvidka declined to comment on the issue when asked by New Scientist.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

Eric Schmidt has prodded the Pentagon for years to hurry along its software-buying process. Today the AI tech investor and former Google CEO is more determined than ever to urge government decision-makers to pick up the pace, but not just when it comes to buying more software for the Defense Department. Schmidt wants the government to implement his sweeping blueprint to fight what he considers an existential threat to democracy posed by China's AI plans, an effort that could also bolster his own commercial AI interests. He says the U.S.'s national security and economic leadership are dependent upon spending billions to procure smarter software, bolster AI research, and build the country's computer science talent pool. And he says he knows better than the Pentagon itself how to remove the bureaucratic blockades preventing more agile use of AI by the government. But at the same time, Schmidt's venture capital firm Innovation Endeavors has invested in companies that have received multimillion-dollar contracts from federal agencies. Some of those investments and contracts -- reported here for the first time -- were granted between 2016 and 2021 while Schmidt chaired two influential government initiatives, the Pentagon's Defense Innovation Board and the National Security Commission on Artificial Intelligence.

In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference bring interpretability to ReLiSt.

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://github.com/OreOZhao/MoSE4MKGC.

Why Tesla Will Never Produce The Roadster Tesla has been teasing the idea of a new Roadster for years, but it looks like the car won't actually be produced. In this article, we explore some of the reasons Tesla might never release a Roadster. Let's talk about what happened with the Tesla Roadster, the naked electric sports car was the car that launched Tesla as a brand and paved the way for Everything we know today and in 2017. Elon Musk revealed the next generation Roadster as it stormed the world with a Tesla semi-truck trailer Elon promised so much to that little car with the fastest production vehicle built from 0-60 in less than two seconds and a top speed of more than 250mph which is the longest range ever in an electric car at 620 Miles per charge and sticker price of just $200,000, which is pennies in the supercar world. The reason why you have a roadster suddenly come back to the top of our mind is that I remembered something Elon told Joe Rogan a couple years ago they were talking about the Roadster and how insane it would be Joe asked when are you going to make this thing and Elon said that he should probably do the semi and the Cyber truck first and then he'd get to the Roadster well the Tesla semi has begun first production and is going out to customers on December 1st.

Thank you so much for joining us in this interview series! Can you share with us the'backstory" of how you decided to pursue this career path in AI? I believe that there is enormous power in data. The more a company has, the more they're able to propel their businesses forward. But only if they're able to get meaningful insights from it.